DESIGNING THE NEXT STUDY FOR MAXIMUM IMPACT

Generalized knowledge comes from cumulating results across studies, a process known as meta-analysis. Efficiently increasing generalized kno wledge in a defined area-estimates of price or advertising, for exampl e-is one important goal for research. Because (I) most meta-analyses a re based on highly inefficient and unbalanced natural experiments or d esigns and (2) additional studies are costly, carefully selecting the next study is important. The authors demonstrate that, rather than sim ply selecting a study that uses currently underrepresented design vari ables, a procedure that reduces collinearity among design variables wi ll produce far superior improvements in knowledge.